a random variable with a finite number of equally likely outcomes is best described by a A binomial distribution B Bernoulli distribuition C. Discrete uniform Distribution D. Continous uniform distribution The answer is C But wouldn’t a binomial distribution fit this description as well? the binomial distribution is easily considered finite because it would be one of two outcomes and each outcome has a 50% probability of being chosen.
If you have three outcomes, the possibility would be 25%, 50%, 25%. That would not be same.
I believe, first of all, the binomial distribution doesn’t have to be 50% 50% chance. (It can be just p and 1-p. Bernoulli distribution is, but even in that case, the distribution is not equal, because for example, you have 2 coin tosses, the chance of getting HH = 1/4, HT = 1/2, TT = 1/4. Please correct me if I’m wrong.
Binomial outcomes do not necessarily have to have a 50% chance of being chosen. Most of the situations we are used to --> coin tossing e.g. have H,T with 50%. But there are plenty of other situations --> e.g. Economy has a chance of Going Up with a 55% probabilty, while it goes down with a 45% probability. That too is binomial, but not equally likely. So Choice C is the right one – in a discrete uniform distribution – all outcomes ARE definitely equally likely. CP
that sounds about right…thanks for clarifiying that for me…so there can be more than 2 variables in the binomial distribution.
I think you are getting confused between variables and outcomes. The one variable was result of coin toss. It had outcomes H and T with 50% probability In the other experiment Variable: Economy rise or fall. Probability: 0.6, 0.4 (or whatever). This question is talking about outcomes.